import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_SCREAMING_SNAKE_CASE     				=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     				=		{'vocab_file': 'spiece.model'}

_SCREAMING_SNAKE_CASE     				=		{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_SCREAMING_SNAKE_CASE     				=		{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_SCREAMING_SNAKE_CASE     				=		0
_SCREAMING_SNAKE_CASE     				=		1
_SCREAMING_SNAKE_CASE     				=		2
_SCREAMING_SNAKE_CASE     				=		3
_SCREAMING_SNAKE_CASE     				=		4



class 							a				(     __lowerCAmelCase			):







 """simple docstring"""






 lowerCamelCase      :List[Any]            =						VOCAB_FILES_NAMES
 lowerCamelCase      :Optional[int]            =						PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase      :List[Any]            =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase      :Tuple            =						'''left'''



 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_=False						,							lowerCAmelCase_=True						,							lowerCAmelCase_=False						,							lowerCAmelCase_="<s>"						,							lowerCAmelCase_="</s>"						,							lowerCAmelCase_="<unk>"						,							lowerCAmelCase_="<sep>"						,							lowerCAmelCase_="<pad>"						,							lowerCAmelCase_="<cls>"						,							lowerCAmelCase_="<mask>"						,							lowerCAmelCase_=["<eop>", "<eod>"]						,							lowerCAmelCase_ = None						,							**lowerCAmelCase_						,							)    ->   None:
        _A	  =    AddedToken(lowercase_						,							lstrip=lowercase_						,							rstrip=lowercase_				) if isinstance(lowercase_						,							lowercase_				) else mask_token

        _A	  =    {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=lowercase_						,							remove_space=lowercase_						,							keep_accents=lowercase_						,							bos_token=lowercase_						,							eos_token=lowercase_						,							unk_token=lowercase_						,							sep_token=lowercase_						,							pad_token=lowercase_						,							cls_token=lowercase_						,							mask_token=lowercase_						,							additional_special_tokens=lowercase_						,							sp_model_kwargs=self.sp_model_kwargs						,							**lowercase_						,							)

        _A	  =    3

        _A	  =    do_lower_case
        _A	  =    remove_space
        _A	  =    keep_accents
        _A	  =    vocab_file

        _A	  =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(lowercase_				)



 @property
 def 	UpperCAmelCase				(   self				)    ->   Dict:
        return len(self.sp_model				)



 def 	UpperCAmelCase				(   self				)    ->   Union[str, Any]:
        _A	  =    {self.convert_ids_to_tokens(lowercase_				): i for i in range(self.vocab_size				)}
        vocab.update(self.added_tokens_encoder				)
        return vocab



 def __getstate__(   self				)    ->   Any:
        _A	  =    self.__dict__.copy()
        _A	  =    None
        return state



 def __setstate__(   self						,							lowerCAmelCase_				)    ->   Dict:
        _A	  =    d

        # for backward compatibility
        if not hasattr(self						,							"""sp_model_kwargs"""				):
               _A	  =    {}

        _A	  =    spm.SentencePieceProcessor(**self.sp_model_kwargs				)
        self.sp_model.Load(self.vocab_file				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Tuple:







        if self.remove_space:
               _A	  =    """ """.join(inputs.strip().split()				)
        else:
               _A	  =    inputs
        _A	  =    outputs.replace("""``"""						,							"""\""""				).replace("""''"""						,							"""\""""				)

        if not self.keep_accents:
               _A	  =    unicodedata.normalize("""NFKD"""						,							lowercase_				)
               _A	  =    """""".join([c for c in outputs if not unicodedata.combining(lowercase_				)]				)
        if self.do_lower_case:
               _A	  =    outputs.lower()

        return outputs



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   List[str]:
        _A	  =    self.preprocess_text(lowercase_				)
        _A	  =    self.sp_model.encode(lowercase_						,							out_type=lowercase_				)
        _A	  =    []
        for piece in pieces:
               if len(lowercase_				) > 1 and piece[-1] == str(""","""				) and piece[-2].isdigit():
                      _A	  =    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_						,							""""""				)				)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]				) == 1:
                                    _A	  =    cur_pieces[1:]
                             else:
                                    _A	  =    cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]				)
                      new_pieces.extend(lowercase_				)
               else:
                      new_pieces.append(lowercase_				)

        return new_pieces



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   List[str]:
        return self.sp_model.PieceToId(lowercase_				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Any:
        return self.sp_model.IdToPiece(lowercase_				)



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   Tuple:
        _A	  =    """""".join(lowercase_				).replace(lowercase_						,							""" """				).strip()
        return out_string



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = False						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = True						,							**lowerCAmelCase_						,							)    ->   str:
        _A	  =    kwargs.pop("""use_source_tokenizer"""						,							lowercase_				)

        _A	  =    self.convert_ids_to_tokens(lowercase_						,							skip_special_tokens=lowercase_				)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        _A	  =    []
        _A	  =    []
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(lowercase_				)				)
                             _A	  =    []
                      sub_texts.append(lowercase_				)
               else:
                      current_sub_text.append(lowercase_				)
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowercase_				)				)

        # Mimic the behavior of the Rust tokenizer:
        # By default, there are no spaces between special tokens
        _A	  =    """""".join(lowercase_				)

        _A	  =    (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               _A	  =    self.clean_up_tokenization(lowercase_				)
               return clean_text
        else:
               return text



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   List[int]:
        _A	  =    [self.sep_token_id]
        _A	  =    [self.cls_token_id]
        if token_ids_a is None:
               return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None						,							lowerCAmelCase_ = False				)    ->   List[int]:







        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowercase_						,							token_ids_a=lowercase_						,							already_has_special_tokens=lowercase_				)

        if token_ids_a is not None:
               return ([0] * len(lowercase_				)) + [1] + ([0] * len(lowercase_				)) + [1, 1]
        return ([0] * len(lowercase_				)) + [1, 1]



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   List[int]:
        _A	  =    [self.sep_token_id]
        _A	  =    [2]

        if token_ids_a is None:
               return len(token_ids_a + sep				) * [0] + cls_segment_id
        return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_						,							lowerCAmelCase_ = None				)    ->   Tuple[str]:







        if not os.path.isdir(lowercase_				):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
               return
        _A	  =    os.path.join(
            lowercase_						,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

        if os.path.abspath(self.vocab_file				) != os.path.abspath(lowercase_				) and os.path.isfile(self.vocab_file				):
               copyfile(self.vocab_file						,							lowercase_				)
        elif not os.path.isfile(self.vocab_file				):
               with open(lowercase_						,							"""wb"""				) as fi:
                      _A	  =    self.sp_model.serialized_model_proto()
                      fi.write(lowercase_				)

        return (out_vocab_file,)





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




